带搬运时间的批量生产制造系统生产作业计划方法

为给考虑搬运时间的批量生产制造系统中的同类型工件制定生产作业计划,对批量工件的加工与搬运时间、搬运车辆调度和工件移动方式决策等问题进行深入研究.构建同类型批量工件在不同移动方式中的加工与搬运时间模型并设计相应的计算流程图.建立基于生产周期和搬运车辆总投入数量两个决策目标的工件移动方式决策模型.研究结果表明:本研究可有效选择工件移动方式,可为考虑搬运时间的同类型批量工件制定精确的生产作业计划,并可对搬运车辆进行合理调度.     

Forward Rates,Monetary Policy and the Economic Cycle

The short end of the yield curve incorporates essential information to forecast central banks' decisions, but in a biased manner. This article proposes a new method to forecast the Fed and the European Central Bank's decision rate by correcting the swap rates for their cyclical economic premium, using an affine term structure model. The corrected yields offer a higher out‐of‐sample forecasting power than the yields themselves. They also deliver forecasts that are either comparable or better than those obtained with a factor‐augmented vector autoregressive model, underlining the fact that yields are likely to contain at least as much information regarding monetary policy as a dataset composed of economic data series. Copyright © 2015 John Wiley & Sons, Ltd.     

基于LWSVR的繁忙机场航班滑出时间预测

针对繁忙机场航班滑出时间预测准确率低的问题,结合局部回归和加权支持向量回归,提出基于局部加权支持向量回归的离港航班滑出时间预测模型。该模型采用K最近邻方法,减小训练样本集容量,并为每个预测样本构建一个预测模型。通过计算训练样本与预测样本间的马氏距离,来优化加权支持向量回归中高斯核加权函数的带宽参数,获得加权系数。结合某机场离港航班数据仿真分析,实验结果表明模型在误差允许范围内的预测准确率达到83.33%,模型更加稳定。     

The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models

This study examines whether the evaluation of a bankruptcy prediction model should take into account the total cost of misclassification. For this purpose, we introduce and apply a validity measure in credit scoring that is based on the total cost of misclassification. Specifically, we use comprehensive data from the annual financial statements of a sample of German companies and analyze the total cost of misclassification by comparing a generalized linear model and a generalized additive model with regard to their ability to predict a company's probability of default. On the basis of these data, the validity measure we introduce shows that, compared to generalized linear models, generalized additive models can reduce substantially the extent of misclassification and the total cost that this entails. The validity measure we introduce is informative and justifies the argument that generalized additive models should be preferred, although such models are more complex than generalized linear models. We conclude that to balance a model's validity and complexity, it is necessary to take into account the total cost of misclassification.     

时间分数阶Sharma-Tasso-Olver方程和Zakharov方程组的新精确解

利用推广的Kudryashov方法, 借助分数阶行波变换和一致分数阶导数, 给出非线性广义时间分数阶Sharma Tasso Olver方程和Zakharov方程组的若干双曲函数形式的精确解.     

保积Hom-δ-李超三系的拟导子和型心

首先给出Hom-δ-李超三系T的概念, 证明T的广义导子之集、 拟导子之集、 导子之集Der(T)、 中心导子之集ZDer(T)、 拟型心QC(T)和型心C(T)均为李超代数. 其次, 证明中心导子代数和型心代数都是Der(T)的理想, 且ZDer(T)=C(T)∩Der(T). 若T的中心为零, 则［C(T),QC(T)]={0}.     

Making the abstract concrete: The role of norms and values in experimental modeling

Experimental modeling is the construction of theoretical models hand in hand with experimental activity. As explained in Section 1, experimental modeling starts with claims about phenomena that use abstract concepts, concepts whose conditions of realization are not yet specified; and it ends with a concrete model of the phenomenon, a model that can be tested against data. This paper argues that this process from abstract concepts to concrete models involves judgments of relevance, which are irreducibly normative. In Section 2, we show, on the basis of several case studies, how these judgments contribute to the determination of the conditions of realization of the abstract concepts and, at the same time, of the quantities that characterize the phenomenon under study. Then, in Section 3, we compare this view on modeling with other approaches that also have acknowledged the role of relevance judgments in science. To conclude, in Section 4, we discuss the possibility of a plurality of relevance judgments and introduce a distinction between locally and generally relevant factors.     

基于RS和广义目标函数法的清水沟地质灾害承灾体易损性评价

选取白龙流域内具有代表性的清水沟小流域,以清水沟承灾体个体为评价单元,基于遥感数据和广义目标函数方法,引入地质灾害直接威胁概率指标,构建了小流域地质灾害易损性评价指标体系和评价方法,开展了清水沟大比例尺易损性评价.结果显示:清水沟小流域易损性为0～0.105的低易损性承灾体面积占全流域面积的88.4%,0.106～0.251的中易损性承灾体面积占10.2%,0.252～0.812的高易损性承灾体面积仅占1.4%.从空间分布上看,低易损性承灾体主要分布在中上游,以草地、林地和耕地为主,高易损性承灾体主要分布在下游,特别是沟口冲积扇上的居民区,以房屋、公路、桥梁为主.流域内滑坡体对于其下方的沟道以及下游的居民区有较大威胁,导致这些区域的易损性显著增加.应当做好对滑坡体的防护措施,并对下游的居民区进行重点保护.     

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

This paper provides clear‐cut evidence that the out‐of‐sample VaR (value‐at‐risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime‐switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime‐switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered. Copyright © 2014 John Wiley & Sons, Ltd.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.